Distance learning

The term distance learning has different meanings to different people and populations.
By presenting international perspectives of distance learning, this book embraces all
those meanings and populations without giving preference to any. In todayʹs global
world where distance providers can address local learning needs, it is important for
distance learning practitioners and researchers as well as higher education
administrators and faculty to have a broad view of how distance learning is
conceptualized, planned and delivered....

Sitting at the intersection between statistics and machine learning, Dynamic Bayesian Networks have been applied with much success in many domains, such as speech recognition, vision, and computational biology. While Natural Language Processing increasingly relies on statistical methods, we think they have yet to use Graphical Models to their full potential. In this paper, we report on experiments in learning edit distance costs using Dynamic Bayesian Networks and present results on a pronunciation classiﬁcation task. ...

Multimedia as we know it has gained tremendous importance over the last decade. It
spans quite a few areas of computer science involving programming, algorithms,
communication technology, various media of communications and so on. It has raised
the quality of communication by adding more than one media of communication such
as audio, video, text, graphics and animation. Its importance in terms of medical
science, engineering, entertainment, education, distance learning, and to all aspects of
life, cannot be overstated.

Several schools utilize distance learning to meet demands for greater variety in their
curriculum. Distance learning entails transmitting a presentation or lecture from one
location to several remote locations for students to observe.
Distance learning offers several benefits to the traditional classroom setting. It provides
for a more effective use of an educator’s time, and allows a specialist's expertise to be
spread across a wider audience.

Progress in Telecommunications and Networking is fostering the development of
high-speed and ubiquitous networks, both wired and wireless, characterized by an
unprecedented degree of transport capacity and flexibility. At the same time,
laboratory equipment for measurement and experimental evaluation of devices and
systems is available, with a wide range of sophistication and complexity. Activities
such as distance learning, performance monitoring and testing may now receive a
support capable of making them truly distributed and highly cooperative....

Distance-learning business education is a resounding success story. In America, specialist universities, such as
the University of Phoenix, have hundreds of thousands of postgraduate business students enrolled. In India
the total is probably in the millions. If, perhaps, the very top tier of universities are yet to offer distance pro-
grammes, still some very notable ones do: Carnegie Mellon or Thunderbird in America, Warwick or Insitito
Empresas in Europe, for example.
Yet students who take their MBAs at a distance can find themselves railing against some intense snobbery.

This case study will be limited to the experience made at the School of Management (MAM) at BTH, predominantly with their MBA programs. MAM has been giving distance courses over the Internet for five years, and video-conference support on some courses for almost as long. So far each teacher has been using the technological platform and pedagogic he or she feels most comfortable with. Due to this policy many teachers have refrained from using high-interactive solutions like videoconferences and streaming.

With the resources provided by communication technologies, E-learning has been employed in multiple universities, as well as in wide range of training centers and schools. This book presents a structured collection of chapters, dealing with the subject and stressing the importance of E-learning. It shows the evolution of E-learning, with discussion about tools, methodologies, improvements and new possibilities for long-distance learning.

Distance Education Programme (DEP)is a major intervention in Sarva Shiksha Abhiyan(SSA) focusing on “strengthening training through distance learning’. SSA calls for the training of a large no. of teachers and other personnel to bring out qualitative improvement in elementary education. As face-to-face approach or the cascade model would be inadequate to carry out the enormous task of training and recurrent training of those associated with elementary.

Education has become the number one demanded commodity for social and economic transformation for both developing and developed economies. Thus the number of persons going and returning to school has become too big to be handled by existing brick and mortar learning institutions. Besides, the majority of lifelong learners do not have the time to become full-time students. Distance education is becoming the solution to the aforementioned challenges. It has been defined as the mode of study where the learner is separated in time and space from the institution and tutors providing the tuition....

Shortage of manually labeled data is an obstacle to supervised relation extraction methods. In this paper we investigate a graph based semi-supervised learning algorithm, a label propagation (LP) algorithm, for relation extraction. It represents labeled and unlabeled examples and their distances as the nodes and the weights of edges of a graph, and tries to obtain a labeling function to satisfy two constraints: 1) it should be ﬁxed on the labeled nodes, 2) it should be smooth on the whole graph. ...

We use machine learning techniques to ﬁnd the best combination of local focus and lexical distance features for identifying the anchor of mereological bridging references. We ﬁnd that using ﬁrst mention, utterance distance, and lexical distance computed using either Google or WordNet results in an accuracy signiﬁcantly higher than obtained in previous experiments.

This paper presents a dependency language model (DLM) that captures linguistic constraints via a dependency structure, i.e., a set of probabilistic dependencies that express the relations between headwords of each phrase in a sentence by an acyclic, planar, undirected graph. Our contributions are three-fold. First, we incorporate the dependency structure into an n-gram language model to capture long distance word dependency. Second, we present an unsupervised learning method that discovers the dependency structure of a sentence using a bootstrapping procedure. ...

It is necessary to have a (large) annotated corpus to build a statistical parser. Acquisition of such a corpus is costly and time-consuming. This paper presents a method to reduce this demand using active learning, which selects what samples to annotate, instead of annotating blindly the whole training corpus. Sample selection for annotation is based upon “representativeness” and “usefulness”. A model-based distance is proposed to measure the difference of two sentences and their most likely parse trees.

We identify four types of errors that unsupervised induction systems make and study each one in turn. Our contributions include (1) using a meta-model to analyze the incorrect biases of a model in a systematic way, (2) providing an efﬁcient and robust method of measuring distance between two parameter settings of a model, and (3) showing that local optima issues which typically plague EM can be somewhat alleviated by increasing the number of training examples. We conduct our analyses on three models: the HMM, the PCFG, and a simple dependency model. ...

Past work on English coordination has focused on coordination scope disambiguation. In Japanese, detecting whether coordination exists in a sentence is also a problem, and the state-of-the-art alignmentbased method specialized for scope disambiguation does not perform well on Japanese sentences. To take the detection of coordination into account, this paper introduces a ‘bypass’ to the alignment graph used by this method, so as to explicitly represent the non-existence of coordinate structures in a sentence.

In this paper, we describe a rote extractor that learns patterns for ﬁnding semantic relationships in unrestricted text, with new procedures for pattern generalization and scoring. These include the use of partof-speech tags to guide the generalization, Named Entity categories inside the patterns, an edit-distance-based pattern generalization algorithm, and a pattern accuracy calculation procedure based on evaluating the patterns on several test corpora.

Judath Ash is a fonner lecturer in academic and medical English at Southwark College. She now writes freelance and is working on distance learning programmes for IELTS and a series of IELTS books.
Judith Ash is co-author of A book for IELTS.